Multisensory Integration in Speed Estimation During Self-Motion
Bibliographic record
Abstract
This study assessed the relative contributions of visual and proprioceptive/motor information during self-motion in a virtual environment using a speed discrimination task. Subjects wore a head-mounted display and rode a stationary bicycle along a straight path in an empty, seemingly infinite hallway with random surface texture. For each trial, subjects were required to pedal the bicycle along two paths at two different speeds (a standard speed and a comparison speed) and subsequently report whether the second speed travelled was faster than the first. The standard speed remained the same while the comparison speed was varied between trials according to the method of constant stimuli. When visual and proprioceptive/motor cues were provided separately or in combination, the speed discrimination thresholds were comparable, suggesting that either cue alone is sufficient. When the relation between visual and proprioceptive information was made inconsistent by varying optic flow gain, the resulting psychometric functions shifted along the horizontal axis (pedalling speed). The degree of separation between these functions indicated that both optic flow and proprioceptive cues contributed to speed estimation, with proprioceptive cues being dominant. These results suggest an important role for proprioceptive information in speed estimation during self-motion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".